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Data development

Our picks

LLM evaluation in enterprise applications: a new era in ML

Learn about the obstacles faced by data scientists in LLM evaluation and discover effective strategies for overcoming them.

November 25, 2024

AI data development: a guide for data science projects

What is AI data development? AI data development includes any action taken to convert raw information into a format useful to AI.

November 13, 2024

Building better enterprise AI: incorporating expert feedback in system development

Enterprises that aim to build valuable GenAI applications must view them from a systems-level. LLMs are just one part of an ecosystem.

January 30, 2024

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Building the Benchmark: Inside Our Agentic Insurance Underwriting Dataset

In this post, we unpack how Snorkel built a realistic benchmark dataset to evaluate AI agents in commercial insurance underwriting. From expert-driven data design to multi-tool reasoning tasks, see how our approach surfaces actionable failure modes that generic benchmarks miss—revealing what it really takes to deploy AI in enterprise workflows.

July 10, 2025

Evaluating AI Agents for Insurance Underwriting

In this post, we will show you a specialized benchmark dataset we developed with our expert network of Chartered Property and Casualty Underwriters (CPCUs). The benchmark uncovers several model-specific and actionable error modes, including basic tool use errors and a surprising number of insidious hallucinations from one provider. This is part of an ongoing series of benchmarks we are releasing across verticals…

June 26, 2025

LLM Observability: Key Practices, Tools, and Challenges

LLM observability is crucial for monitoring, debugging, and improving large language models. Learn key practices, tools, and strategies of LLM observability.

June 23, 2025

All articles on
Data development

Building the Benchmark: Inside Our Agentic Insurance Underwriting Dataset

In this post, we unpack how Snorkel built a realistic benchmark dataset to evaluate AI agents in commercial insurance underwriting. From expert-driven data design to multi-tool reasoning tasks, see how our approach surfaces actionable failure modes that generic benchmarks miss—revealing what it really takes to deploy AI in enterprise workflows.

July 10, 2025

Evaluating AI Agents for Insurance Underwriting

In this post, we will show you a specialized benchmark dataset we developed with our expert network of Chartered Property and Casualty Underwriters (CPCUs). The benchmark uncovers several model-specific and actionable error modes, including basic tool use errors and a surprising number of insidious hallucinations from one provider. This is part of an ongoing series of benchmarks we are releasing across verticals…

June 26, 2025

LLM Observability: Key Practices, Tools, and Challenges

LLM observability is crucial for monitoring, debugging, and improving large language models. Learn key practices, tools, and strategies of LLM observability.

June 23, 2025

LLM-as-a-judge for enterprises: evaluate model alignment at scale

Discover how enterprises can leverage LLM-as-Judge systems to evaluate generative AI outputs at scale, improve model alignment, reduce costs, and tackle challenges like bias and interpretability.

March 26, 2025

Why enterprises should embrace LLM distillation

Unlock possibilities for your enterprise with LLM distillation. Learn how distilled, task-specific models boost performance and shrink costs.

February 18, 2025

LLM evaluation in enterprise applications: a new era in ML

Learn about the obstacles faced by data scientists in LLM evaluation and discover effective strategies for overcoming them.

November 25, 2024

AI data development: a guide for data science projects

What is AI data development? AI data development includes any action taken to convert raw information into a format useful to AI.

November 13, 2024

How a global financial services company built a specialized AI copilot accurate enough for production

Learn how Snorkel, Databricks, and AWS enabled the team to build and deploy small, specialized, and highly accurate models which met their AI production requirements and strategic goals.

Dr. Bubbles, Snorkel AI's mascot
September 9, 2024

Task Me Anything: innovating multimodal model benchmarks

“Task Me Anything” empowers data scientists to generate bespoke benchmarks to assess and choose the right multimodal model for their needs.

September 4, 2024

Alfred: Data labeling with foundation models and weak supervision

Introducing Alfred: an open-source tool for combining foundation models with weak supervision for faster development of academic data sets.

August 27, 2024

New GenAI features, data annotation: Snorkel Flow 2024.R2

This release features new GenAI tools and Multi-Schema Annotation, as well as new enterprise security tools and an updated home page.

August 7, 2024

How data slices transform enterprise LLM evaluation

Enterprises must evaluate LLM performance for production deployment. Custom, automated eval + data slices present the best path to production.

August 1, 2024

Meta’s Llama 3.1 405B is the new Mr. Miyagi, now what?

Meta’s Llama 3.1 405B, rivals GPT-4o in benchmarks, offering powerful AI capabilities. Despite high costs, it can enhance LLM adoption through fine-tuning, distillation, and as an AI judge.

July 25, 2024

Meta’s new Llama 3.1 models are here! Are you ready for it?

Meta released Llama 3 405B today, signaling a new era of open source AI. The model is ready to use on Snorkel Flow.

July 23, 2024

Data-centric AI with Snorkel and MinIO

High-performing AI systems require more than a well-designed model. They also require properly constructed training and testing data.

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